Achieve Load Balancing in Multi-UAV Edge Computing IoT Networks: A Dynamic Entry and Exit Mechanism

Guo, Hongzhi; Zhou, Xiaoyi; Wang, Yutao; Li, Jiajia

doi:10.1109/jiot.2022.3161703

Cited by 34 publications

(8 citation statements)

References 45 publications

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“…Although few studies such as [228] [229] [230] [231] have considered load balancing routing protocols to address both complicated dynamic network environments as well as network traffic increase in future, it can be concluded from the comparison that majority of routing protocols do not take traffic load balancing into account [232]. Most of the routing protocols including multi-path routing have not been able to effectively balance the load of network as well as energy utilization.…”

Section: ) Routing Protocols Discussionmentioning

confidence: 99%

Unmanned Aerial Vehicle Communications for Civil Applications: A Review

et al. 2022

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The use of drones, formally known as unmanned aerial vehicles (UAVs), has significantly increased across a variety of applications over the past few years. This is due to the rapid advancement towards the design and production of inexpensive and dependable UAVs and the growing request for the utilization of such platforms particularly in civil applications. With their intrinsic attributes such as high mobility, rapid deployment and flexible altitude, UAVs have the potential to be utilized in many wireless system applications. On the one hand, UAVs are able to operate as flying mobile terminals within wireless/cellular networks to support a variety of missions such as goods delivery, search and rescue, precision agriculture monitoring, and remote sensing. On the other hand, UAVs can be utilized as aerial base stations to increase wireless communication coverage, reliability, and the capacity of wireless systems without additional investment in wireless systems infrastructure. The aim of this article is to review the current applications of UAVs for civil and commercial purposes. The focus of this paper is on the challenges and communication requirements associated with UAV-based communication systems. This article initially classifies UAVs in terms of various parameters, some of which can impact UAVs' communication performance. It then provides an overview of aerial networking and investigates UAVs routing protocols specifically, which are considered as one of the challenges in UAV communication. This article later investigates the use of UAV networks in a variety of civil applications and considers many challenges and communication demands of these applications. Subsequently, different types of simulation platforms are investigated from a communication and networking viewpoint. Finally, it identifies areas of future research.

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Section: ) Routing Protocols Discussionmentioning

confidence: 99%

Unmanned Aerial Vehicle Communications for Civil Applications: A Review

et al. 2022

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“…Their approach uses an adaptive reinforcement learning algorithm that learns the optimal trajectory of the UAV based on a QoS-aware reward function. Similarly, Guo et al focus on dynamic entry and exit for balanced offloading in IoT networks [29]. They propose a trajectory optimization scheme that maximizes the energy efficiency of the UAV while considering real-time need and distribution of ground users.…”

Section: A Uav Trajectory Designmentioning

confidence: 99%

Resource Scheduling Based on Deep Reinforcement Learning in UAV Assisted Emergency Communication Networks

Wang

Deng

et al. 2022

IEEE Trans. Commun.

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The advent of fifth generation (5G) networks has opened new avenues for enhancing connectivity, particularly in challenging environments like remote areas or disaster-struck regions. Unmanned aerial vehicles (UAVs) have been identified as a versatile tool in this context, particularly for improving network performance through the Integrated access and backhaul (IAB) feature of 5G. However, existing approaches to UAV-assisted network enhancement face limitations in dynamically adapting to varying user locations and network demands. This paper introduces a novel approach leveraging deep reinforcement learning (DRL) to optimize UAV placement in real-time, dynamically adjusting to changing network conditions and user requirements. Our method focuses on the intricate balance between fronthaul and backhaul links, a critical aspect often overlooked in current solutions. The unique contribution of this work lies in its ability to autonomously position UAVs in a way that not only ensures robust connectivity to ground users but also maintains seamless integration with central network infrastructure. Through various simulated scenarios, we demonstrate how our approach effectively addresses these challenges, enhancing coverage and network performance in critical areas. This research fills a significant gap in UAV-assisted 5G networks, providing a scalable and adaptive solution for future mobile networks.

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“…After completing the trip, the information regarding the flight can be monitored in real-time with the utilization of IoT systems in which the essential information is gathered utilizing sensors. 2 Presently, the utilization of actuators and sensors via novel networks like flying ad hoc networks (FANETs) creates a tremendous impact on applications such as smart agriculture. 3,4 The development of drone-aided FANET makes present agriculture smarter by managing the diverse issues that are faced by the farmers like pest and disease identification in plants, detecting the presence of parasites, forecasting the immediate climate changes, and so on.…”

Section: Introductionmentioning

confidence: 99%

“…In aeronautics, the internet is adopted to link the sensors and actuators for viewing and transmitting the information inside the aerial objects such as drones and satellites. After completing the trip, the information regarding the flight can be monitored in real‐time with the utilization of IoT systems in which the essential information is gathered utilizing sensors 2 . Presently, the utilization of actuators and sensors via novel networks like flying ad hoc networks (FANETs) creates a tremendous impact on applications such as smart agriculture 3,4 .…”

Section: Introductionmentioning

confidence: 99%

PCSSHO: An implementation of Percentage of Circle Search and Spotted Hyena Optimizer for multi‐constraint load balancing and routing framework in FANET

Ilango,

Sivarajan,

RaviChandran

et al. 2024

Int J Communication

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SummaryFlying ad hoc network (FANET) is a technology that has seen tremendous growth in the past years due to its application in various military and civil developments. Conventional topology‐aided routing schemes are not suitable for large‐scale FANETs. This is because of the higher mobility rate in UAVs with the architecture differences. Hence, several path entries in routing become invalid and the neighboring nodes can be occupied before the interruption. Hence, it is very important to solve the afore‐explained issues in FANET. Initially, the task is assigned to the UAVs. These tasks are performed by satisfying the demands such as timing, congestion, and energy to attain the effective load‐balancing performance. Then, the optimal clustering and cluster head (CH) selection are performed using the developed Percentage of Circle Search and Spotted Hyena Optimizer (PCSSHO) in the communication path. Here, the feature selection is done based on constraints like position, speed, moving direction, height variation, link quality, and inter‐ and intra‐cluster distance. Then, the developed PCSSHO model performs routing by considering different constraints such as “end‐to‐end delay, delivery ratio, power consumption, and link quality.” Thus, the recommenced load balancing model in FANET secures an enhanced performance rate than the conventional load balancing frameworks.

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Achieve Load Balancing in Multi-UAV Edge Computing IoT Networks: A Dynamic Entry and Exit Mechanism

Cited by 34 publications

References 45 publications

Unmanned Aerial Vehicle Communications for Civil Applications: A Review

Unmanned Aerial Vehicle Communications for Civil Applications: A Review

Resource Scheduling Based on Deep Reinforcement Learning in UAV Assisted Emergency Communication Networks

PCSSHO: An implementation of Percentage of Circle Search and Spotted Hyena Optimizer for multi‐constraint load balancing and routing framework in FANET

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